Exploring the Role of Representation in Models of Grammatical Category Acquisition

Ting Qian, University of Rochester

Patricia Reeder, University of Rochester

Richard Aslin, University of Rochester

Joshua Tenenbaum, MIT

Elissa Newport, University of Rochester

Abstract

One major aspect of successful language acquisition is the ability
to generalize from properties of experienced items to novel items. We present a
computational study of artificial language learning, where the generalization
patterns of three generative models are compared to those of human learners
across 10 experiments. Results suggest that an explicit representation of word
categories is the best model for capturing the generalization patterns of human
learners across a wide range of learning environments. We discuss the
representational assumptions implied by these models.